Ripple-Pivot Search: Active Parallel Decoding for Diffusion Large Language Models
Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive language models, offering the potential for substantially faster inference through parallel decoding. Existing parallel decoding schedulers typically commit positions only after they meet a per-position criterion, overlooking how early commitments may benefit subsequent decoding. We identify a ripple effect in dLLM decoding: proactively committing a mid-entropy pivot position can induce a pronounced reduction in uncertainty across the remaining masked positions. This uncertainty reduction allows subsequent steps to unmask more tokens in parallel, thereby accelerating the overall decoding pr...
| Authors | Yushi Ye, Xu Chen, Haoyun Jiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 12 August 2026 |
| Last updated at source | 18 September 2026 |
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